Chidubem Iddianozie
Biographic Data
| ID | 9445904 |
|---|---|
| NAME | Chidubem Iddianozie |
| GIVEN NAMES | Chidubem |
| FAMILY NAME | Iddianozie |
| SIGNATURE | IDDIANOZIE C |
| AFFILIATIONS | University College Dublin |
| ORCID | 0000-0001-8677-3159 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
Improved Graph Neural Networks for Spatial Networks Using Structure-Aware Sampling
Graph Neural Networks (GNNs) have received wide acclaim in recent times due to their performance on inference tasks for unstructured data. Typically, GNNs operate by exploiting local structural information in graphs and disregarding their global structure. This is influenced by assumptions of homophily and unbiased class distributions. As a result, this could impede model performance on noisy real-world graphs such as spatial graphs where these a…
Towards smart sustainable cities
Building Management Systems (BMS) are crucial in the drive towards smart sustainable cities. This is due to the fact that they have been effective in significantly reducing the energy consumption of buildings. A typical BMS is composed of smart devices that communicate with one another in order to achieve their purpose. However, the heterogeneity of these devices and their associated meta-data impede the deployment of solutions that depend on the…
No prominent works on this page.
Improved Graph Neural Networks for Spatial Networks Using Structure-Aware Sampling
Graph Neural Networks (GNNs) have received wide acclaim in recent times due to their performance on inference tasks for unstructured data. Typically, GNNs operate by exploiting local structural information in graphs and disregarding their global structure. This is influenced by assumptions of homophily and unbiased class distributions. As a result, this could impede model performance on noisy real-world graphs such as spatial graphs where these a…
Towards smart sustainable cities
Building Management Systems (BMS) are crucial in the drive towards smart sustainable cities. This is due to the fact that they have been effective in significantly reducing the energy consumption of buildings. A typical BMS is composed of smart devices that communicate with one another in order to achieve their purpose. However, the heterogeneity of these devices and their associated meta-data impede the deployment of solutions that depend on the…
Inference (2 works) · Advanced Graph Neural Networks (1 work) · Artificial Intelligence (1 work) · Artificial neural network (1 work) · Building automation (1 work) · Building management system (1 work) · Complex Network Analysis Techniques (1 work) · Computer Science (1 work) · Data mining (1 work) · Discriminative model (1 work)